An Intelligent Visualisation Tool to Analyse the Sustainability of Road Transportation

Carlos Alonso de Armiño, Daniel Urda, Roberto Alcalde, Santiago García and Álvaro Herrero
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Carlos Alonso de Armiño: Departamento de Ingeniería de Organización, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria S/N, 09006 Burgos, Spain
Daniel Urda: Grupo de Inteligencia Computacional Aplicada-GICAP, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria S/N, 09006 Burgos, Spain
Roberto Alcalde: Departamento de Economía y Administración de Empresas, Facultad de Ciencias Económicas y Empresariales, Universidad de Burgos, Pza. de la Infanta Dª. Elena, S/N, 09001 Burgos, Spain
Santiago García: Departamento de Ingeniería de Organización, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria S/N, 09006 Burgos, Spain
Álvaro Herrero: Grupo de Inteligencia Computacional Aplicada-GICAP, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria S/N, 09006 Burgos, Spain

Sustainability, 2022, vol. 14, issue 2, 1-15

Abstract: Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation.

Keywords: artificial intelligence; unsupervised machine learning; exploratory projection pursuit; clustering; road transportation; transport sustainability; age of transport means (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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